Make GraphSage test more robust

Before it failed on MacOS

Discovered by Iavkan in #199

Author: FlorentinD

algo/src/test/java/org/neo4j/gds/embeddings/graphsage/GraphSageModelTrainerTest.java

@@ -25,7 +25,6 @@
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.params.ParameterizedTest;
-import org.junit.jupiter.params.provider.CsvSource;
 import org.junit.jupiter.params.provider.ValueSource;
 import org.neo4j.gds.Orientation;
 import org.neo4j.gds.api.Graph;
@@ -318,33 +317,32 @@ void testConvergence() {
         assertThat(trainMetrics.ranIterationsPerEpoch()).containsExactly(2);
     }
 
-    @ParameterizedTest
-    @CsvSource({
-        "0.01, false, 10",
-        "1.0, true, 7"
-    })
-    void batchesPerIteration(double batchSamplingRatio, boolean expectedConvergence, int expectedRanEpochs) {
-        var trainer = new GraphSageModelTrainer(
-            configBuilder.modelName("convergingModel:)")
-                .maybeBatchSamplingRatio(batchSamplingRatio)
-                .embeddingDimension(12)
-                .aggregator(AggregatorType.POOL)
-                .epochs(10)
-                .tolerance(1e-10)
-                .sampleSizes(List.of(5, 3))
-                .batchSize(5)
-                .maxIterations(100)
-                .randomSeed(42L)
-                .build(),
+    @Test
+    void batchesPerIteration() {
+        configBuilder.modelName("convergingModel:)")
+            .embeddingDimension(2)
+            .aggregator(AggregatorType.POOL)
+            .epochs(10)
+            .tolerance(1e-5)
+            .sampleSizes(List.of(1))
+            .batchSize(5)
+            .maxIterations(100)
+            .randomSeed(42L);
+
+        var trainResultWithoutSampling = new GraphSageModelTrainer(
+            configBuilder.maybeBatchSamplingRatio(1.0).build(),
             Pools.DEFAULT,
             ProgressTracker.NULL_TRACKER
-        );
+        ).train(unweightedGraph, features);
 
-        var trainResult = trainer.train(unweightedGraph, features);
+        var trainResultWithSampling = new GraphSageModelTrainer(
+            configBuilder.maybeBatchSamplingRatio(0.01).build(),
+            Pools.DEFAULT,
+            ProgressTracker.NULL_TRACKER
+        ).train(unweightedGraph, features);
 
-        var trainMetrics = trainResult.metrics();
-        assertThat(trainMetrics.didConverge()).isEqualTo(expectedConvergence);
-        assertThat(trainMetrics.ranEpochs()).isEqualTo(expectedRanEpochs);
+        // reason: sampling results in more stochastic gradient descent and different losses
+        assertThat(trainResultWithoutSampling.metrics().epochLosses().get(0)).isNotEqualTo(trainResultWithSampling.metrics().epochLosses().get(0));
     }
 
     @ParameterizedTest